System for optimized archival using data detection and classification model

ABSTRACT

Systems, computer program products, and methods are described herein for optimized data archival using data detection and classification model. The present invention is configured to receive information associated with a first data element within a distributed network environment; determine a first data type associated with the first data element; determine one or more archival actions associated with the first data element; determine one or more archival requirements associated with the first data element; determine one or more utilization parameters associated with the first data element; initiate an execution of the one or more archiving actions on the first data element; determine that the one or more archival actions meet the one or more archival requirements associated with the first data element; and execute the one or more archiving actions based on at least determining that the one or more archival actions meet the one or more archival requirements.

FIELD OF THE INVENTION

The present invention embraces a system for optimized data archivalusing data detection and classification model.

BACKGROUND

Data archiving is the practice of identifying and moving data elementsfrom a network environment into target archives for long term retention.As data grows, archiving data has become an important consideration forentities as part of a robust data management strategy. However, handlingdata elements for archiving depends on data characteristics such as datatype, data format (structured or unstructured), data volume, datapatterns, and/or the like. These data characteristics define the type ofarchiving actions that are required to archive the data element for longterm storage and retention. Therefore, there is a need for an archivalmanagement system that optimizes the archival strategy using a datadetection and classification model to achieve seamless management andtransition of data elements from a network environment to targetarchives for long term retention.

SUMMARY

The following presents a simplified summary of one or more embodimentsof the present invention, in order to provide a basic understanding ofsuch embodiments. This summary is not an extensive overview of allcontemplated embodiments and is intended to neither identify key orcritical elements of all embodiments nor delineate the scope of any orall embodiments. Its sole purpose is to present some concepts of one ormore embodiments of the present invention in a simplified form as aprelude to the more detailed description that is presented later.

In one aspect, a system for optimized data archival using data detectionand classification model is presented. The system comprising: at leastone non-transitory storage device; and at least one processing devicecoupled to the at least one non-transitory storage device, wherein theat least one processing device is configured to: electronically receiveinformation associated with a first data element within a distributednetwork environment; determine a first data type associated with thefirst data element based on at least the information associated with thefirst data element; determine one or more archival actions associatedwith the first data element based on at least the first data type;determine one or more archival requirements associated with the firstdata element; determine one or more utilization parameters associatedwith the first data element; initiate an execution of the one or morearchiving actions on the first data element based on at least the one ormore utilization parameters associated with the first data element;determine that the one or more archival actions meet the one or morearchival requirements associated with the first data element; andexecute the one or more archiving actions based on at least determiningthat the one or more archival actions meet the one or more archivalrequirements.

In some embodiments, the at least one processing device is furtherconfigured to: retrieve metadata associated with the execution of theone or more archiving actions; and initiate a dashboard report script,wherein the dashboard report script is configured to generate agraphical interface for display on a computing device of a user, whereinthe graphical interface comprises the metadata associated with theexecution of the one or more archiving actions.

In some embodiments, the at least one processing device is furtherconfigured to: electronically receive information associated with one ormore data elements within the distributed network environment;electronically receive, from the computing device of the user, the oneor more data types associated with the one or more data elements; andgenerate a training dataset based on at least the information associatedwith the one or more data elements and the one or more data types.

In some embodiments, the at least one processing device is furtherconfigured to: initiate one or more machine learning algorithms on thetraining dataset; and generate a machine learning model based on atleast initiating the one or more machine learning algorithms on thetraining dataset.

In some embodiments, the at least one processing device is furtherconfigured to: electronically receive information associated with thefirst data element; and classify, using the machine learning model, thefirst data element into the first data type.

In some embodiments, the at least one processing device is furtherconfigured to: determine one or more archival actions associated withthe first data element, wherein determining further comprises:determining one or more target archives associated with the first dataelement based on at least the first data type; and retrieving, from aheader portion of the first data element, data security level associatedwith the first data element.

In some embodiments, wherein the at least one processing device isfurther configured to: continuously scan the distributed networkenvironment; determine a data usage pattern associated with the firstdata element based on at least continuously scanning the distributednetwork environment; and determine one or more utilization parametersassociated with the first data element based on at least the data usagepattern.

In some embodiments, the one or more utilization parameters comprises atleast an idle time and a minimal disruption time period.

In another aspect, a computer program product for optimized dataarchival using data detection and classification model is presented. Thecomputer program product comprising a non-transitory computer-readablemedium comprising code causing a first apparatus to: electronicallyreceive information associated with a first data element within adistributed network environment; determine a first data type associatedwith the first data element based on at least the information associatedwith the first data element; determine one or more archival actionsassociated with the first data element based on at least the first datatype; determine one or more archival requirements associated with thefirst data element; determine one or more utilization parametersassociated with the first data element; initiate an execution of the oneor more archiving actions on the first data element based on at leastthe one or more utilization parameters associated with the first dataelement; determine that the one or more archival actions meet the one ormore archival requirements associated with the first data element; andexecute the one or more archiving actions based on at least determiningthat the one or more archival actions meet the one or more archivalrequirements.

In yet another aspect, a method for optimized data archival using datadetection and classification model is presented. The method comprising:electronically receiving information associated with a first dataelement within a distributed network environment; determining a firstdata type associated with the first data element based on at least theinformation associated with the first data element; determining one ormore archival actions associated with the first data element based on atleast the first data type; determining one or more archival requirementsassociated with the first data element; determining one or moreutilization parameters associated with the first data element;initiating an execution of the one or more archiving actions on thefirst data element based on at least the one or more utilizationparameters associated with the first data element; determining that theone or more archival actions meet the one or more archival requirementsassociated with the first data element; and executing the one or morearchiving actions based on at least determining that the one or morearchival actions meet the one or more archival requirements.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined with yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made the accompanying drawings, wherein:

FIG. 1 illustrates technical components of a system for optimized dataarchival using data detection and classification model, in accordancewith an embodiment of the invention;

FIG. 2 illustrates a process flow for optimized data archival using datadetection and classification model, in accordance with an embodiment ofthe invention; and

FIG. 3 illustrates a data flow diagram for optimized data archival usingdata detection and classification model, in accordance with anembodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Where possible, any terms expressed in the singularform herein are meant to also include the plural form and vice versa,unless explicitly stated otherwise. Also, as used herein, the term “a”and/or “an” shall mean “one or more,” even though the phrase “one ormore” is also used herein. Furthermore, when it is said herein thatsomething is “based on” something else, it may be based on one or moreother things as well. In other words, unless expressly indicatedotherwise, as used herein “based on” means “based at least in part on”or “based at least partially on.” Like numbers refer to like elementsthroughout.

As used herein, an “entity” may be any institution employing informationtechnology resources and particularly technology infrastructureconfigured for processing large amounts of data. Typically, these datacan be related to the people who work for the organization, its productsor services, the customers or any other aspect of the operations of theorganization. As such, the entity may be any institution, group,association, financial institution, establishment, company, union,authority or the like, employing information technology resources forprocessing large amounts of data.

As described herein, a “user” may be an individual associated with anentity. As such, in some embodiments, the user may be an individualhaving past relationships, current relationships or potential futurerelationships with an entity. In some embodiments, a “user” may be anemployee (e.g., an associate, a project manager, an IT specialist, amanager, an administrator, an internal operations analyst, or the like)of the entity or enterprises affiliated with the entity, capable ofoperating the systems described herein. In some embodiments, a “user”may be any individual, entity or system who has a relationship with theentity, such as a customer or a prospective customer. In otherembodiments, a user may be a system performing one or more tasksdescribed herein.

As used herein, a “user interface” may be any device or software thatallows a user to input information, such as commands or data, into adevice, or that allows the device to output information to the user. Forexample, the user interface includes a graphical user interface (GUI) oran interface to input computer-executable instructions that direct aprocessing device to carry out specific functions. The user interfacetypically employs certain input and output devices to input datareceived from a user second user or output data to a user. These inputand output devices may include a display, mouse, keyboard, button,touchpad, touch screen, microphone, speaker, LED, light, joystick,switch, buzzer, bell, and/or other user input/output device forcommunicating with one or more users.

As used herein, an “engine” may refer to core elements of a computerprogram, or part of a computer program that serves as a foundation for alarger piece of software and drives the functionality of the software.An engine may be self-contained, but externally-controllable code thatencapsulates powerful logic designed to perform or execute a specifictype of function. In one aspect, an engine may be underlying source codethat establishes file hierarchy, input and output methods, and how aspecific part of a computer program interacts or communicates with othersoftware and/or hardware. The specific components of an engine may varybased on the needs of the specific computer program as part of thelarger piece of software. In some embodiments, an engine may beconfigured to retrieve resources created in other computer programs,which may then be ported into the engine for use during specificoperational aspects of the engine. An engine may be configurable to beimplemented within any general purpose computing system. In doing so,the engine may be configured to execute source code embedded therein tocontrol specific features of the general purpose computing system toexecute specific computing operations, thereby transforming the generalpurpose system into a specific purpose computing system.

As used herein, “authentication credentials” may be any information thatcan be used to identify of a user. For example, a system may prompt auser to enter authentication information such as a username, a password,a personal identification number (PIN), a passcode, biometricinformation (e.g., iris recognition, retina scans, fingerprints, fingerveins, palm veins, palm prints, digital bone anatomy/structure andpositioning (distal phalanges, intermediate phalanges, proximalphalanges, and the like), an answer to a security question, a uniqueintrinsic user activity, such as making a predefined motion with a userdevice. This authentication information may be used to authenticate theidentity of the user (e.g., determine that the authenticationinformation is associated with the account) and determine that the userhas authority to access an account or system. In some embodiments, thesystem may be owned or operated by an entity. In such embodiments, theentity may employ additional computer systems, such as authenticationservers, to validate and certify resources inputted by the plurality ofusers within the system. The system may further use its authenticationservers to certify the identity of users of the system, such that otherusers may verify the identity of the certified users. In someembodiments, the entity may certify the identity of the users.Furthermore, authentication information or permission may be assigned toor required from a user, application, computing node, computing cluster,or the like to access stored data within at least a portion of thesystem.

It should also be understood that “operatively coupled,” as used herein,means that the components may be formed integrally with each other, ormay be formed separately and coupled together. Furthermore, “operativelycoupled” means that the components may be formed directly to each other,or to each other with one or more components located between thecomponents that are operatively coupled together. Furthermore,“operatively coupled” may mean that the components are detachable fromeach other, or that they are permanently coupled together. Furthermore,operatively coupled components may mean that the components retain atleast some freedom of movement in one or more directions or may berotated about an axis (i.e., rotationally coupled, pivotally coupled).Furthermore, “operatively coupled” may mean that components may beelectronically connected and/or in fluid communication with one another.

As used herein, an “interaction” may refer to any communication betweenone or more users, one or more entities or institutions, and/or one ormore devices, nodes, clusters, or systems within the system environmentdescribed herein. For example, an interaction may refer to a transfer ofdata between devices, an accessing of stored data by one or more nodesof a computing cluster, a transmission of a requested task, or the like.

As used herein, “machine learning algorithms” may refer to programs(math and logic) that are configured to self-adjust and perform betteras they are exposed to more data. To this extent, machine learningalgorithms are capable of adjusting their own parameters, given feedbackon previous performance in making prediction about a dataset. Machinelearning algorithms contemplated, described, and/or used herein includesupervised learning (e.g., using logistic regression, using backpropagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, reinforcement learning(e.g., using a Q-learning algorithm, using temporal differencelearning), and/or any other suitable machine learning model type. Eachof these types of machine learning algorithms can implement any of oneor more of a regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial least squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, bootstrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and/or any suitable form of machine learning algorithm.

As used herein, “machine learning model” may refer to a mathematicalmodel generated by machine learning algorithms based on sample data,known as training data, to make predictions or decisions without beingexplicitly programmed to do so. The machine learning model representswhat was learned by the machine learning algorithm and represents therules, numbers, and any other algorithm-specific data structuresrequired to for classification.

Data archiving is the practice of identifying and moving data elementsfrom a network environment into target archives for long term retention.As data grows, archiving data has become an important consideration forentities as part of a robust data management strategy. However, handlingdata elements for archiving depends on data characteristics such as datatype, data format (structured or unstructured), data volume, datapatterns, and/or the like. These data characteristics define the type ofarchiving actions that are required to archive the data element for longterm storage and retention. Therefore, there is a need for an archivalmanagement system that optimizes the archival strategy using a datadetection and classification model to achieve seamless management andtransition of data elements from a network environment to targetarchives for long term retention.

The present invention addresses this requirement by implementing a suiteof solutions capable of identifying the data type and a correspondingset of archival actions required to archive the data element withminimal load on the network environment. In addition, the presentinvention allows for continuous monitoring of the network environment toidentify specific archival means (timing) for each data element based onthe usage pattern of the data element within the network environment.Furthermore, the present invention provides the functional benefit ofidentifying archival requirements based on the data security level ofthe data element and ensuring that the archival actions meet thearchival requirements. Finally, the present invention allows for acustomizable dashboard preview of the archived data for querying,viewing, and interpretation.

FIG. 1 presents an exemplary block diagram of the system environment foroptimized data archival using data detection and classification model100, in accordance with an embodiment of the invention. FIG. 1 providesa unique system that includes specialized servers and systemcommunicably linked across a distributive network of nodes required toperform the functions of the process flows described herein inaccordance with embodiments of the present invention. For purposes ofthe invention, portions of the system environment may also be referredto as a distributed network environment or network environment.

As illustrated, the system environment 100 includes a network 110, asystem 130, and a user input system 140. In some embodiments, the system130, and the user input system 140 may be used to implement theprocesses described herein, in accordance with an embodiment of thepresent invention. In this regard, the system 130 and/or the user inputsystem 140 may include one or more applications stored thereon that areconfigured to interact with one another to implement any one or moreportions of the various user interfaces and/or process flow describedherein.

In accordance with embodiments of the invention, the system 130 isintended to represent various forms of digital computers, such aslaptops, desktops, video recorders, audio/video player, radio,workstations, personal digital assistants, servers, wearable devices,Internet-of-things devices, augmented reality (AR) devices, virtualreality (VR) devices, extended reality (XR) devices automated tellermachine devices, electronic kiosk devices, blade servers, mainframes, orany combination of the aforementioned. In accordance with embodiments ofthe invention, the user input system 140 is intended to representvarious forms of mobile devices, such as personal digital assistants,cellular telephones, smartphones, and other similar computing devices.The components shown here, their connections and relationships, andtheir functions, are meant to be exemplary only, and are not meant tolimit implementations of the inventions described and/or claimed in thisdocument.

In accordance with some embodiments, the system 130 may include aprocessor 102, memory 104, a storage device 106, a high-speed interface108 connecting to memory 104, and a low-speed interface 112 connectingto low speed bus 114 and storage device 106. Each of the components 102,104, 106, 108, 111, and 112 are interconnected using various buses, andmay be mounted on a common motherboard or in other manners asappropriate. The processor 102 can process instructions for executionwithin the system 130, including instructions stored in the memory 104or on the storage device 106 to display graphical information for a GUIon an external input/output device, such as display 116 coupled to ahigh-speed interface 108. In other implementations, multiple processorsand/or multiple buses may be used, as appropriate, along with multiplememories and types of memory. Also, multiple systems, same or similar tosystem 130 may be connected, with each system providing portions of thenecessary operations (e.g., as a server bank, a group of blade servers,or a multi-processor system). In some embodiments, the system 130 may bea server managed by the business. The system 130 may be located at thefacility associated with the business or remotely from the facilityassociated with the business.

The memory 104 stores information within the system 130. In oneimplementation, the memory 104 is a volatile memory unit or units, suchas volatile random access memory (RAM) having a cache area for thetemporary storage of information. In another implementation, the memory104 is a non-volatile memory unit or units. The memory 104 may also beanother form of computer-readable medium, such as a magnetic or opticaldisk, which may be embedded and/or may be removable. The non-volatilememory may additionally or alternatively include an EEPROM, flashmemory, and/or the like. The memory 104 may store any one or more ofpieces of information and data used by the system in which it resides toimplement the functions of that system. In this regard, the system maydynamically utilize the volatile memory over the non-volatile memory bystoring multiple pieces of information in the volatile memory, therebyreducing the load on the system and increasing the processing speed.

The storage device 106 is capable of providing mass storage for thesystem 130. In one aspect, the storage device 106 may be or contain acomputer-readable medium, such as a floppy disk device, a hard diskdevice, an optical disk device, or a tape device, a flash memory orother similar solid state memory device, or an array of devices,including devices in a storage area network or other configurations. Acomputer program product can be tangibly embodied in an informationcarrier. The computer program product may also contain instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The information carrier may be a non-transitorycomputer- or machine-readable storage medium, such as the memory 104,the storage device 104, or memory on processor 102.

In some embodiments, the system 130 may be configured to access, via thenetwork 110, a number of other computing devices (not shown) in additionto the user input system 140. In this regard, the system 130 may beconfigured to access one or more storage devices and/or one or morememory devices associated with each of the other computing devices. Inthis way, the system 130 may implement dynamic allocation andde-allocation of local memory resources among multiple computing devicesin a parallel or distributed system. Given a group of computing devicesand a collection of interconnected local memory devices, thefragmentation of memory resources is rendered irrelevant by configuringthe system 130 to dynamically allocate memory based on availability ofmemory either locally, or in any of the other computing devicesaccessible via the network. In effect, it appears as though the memoryis being allocated from a central pool of memory, even though the spaceis distributed throughout the system. This method of dynamicallyallocating memory provides increased flexibility when the data sizechanges during the lifetime of an application and allows memory reusefor better utilization of the memory resources when the data sizes arelarge.

The high-speed interface 108 manages bandwidth-intensive operations forthe system 130, while the low speed controller 112 manages lowerbandwidth-intensive operations. Such allocation of functions isexemplary only. In some embodiments, the high-speed interface 108 iscoupled to memory 104, display 116 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 111, which may acceptvarious expansion cards (not shown). In such an implementation,low-speed controller 112 is coupled to storage device 106 and low-speedexpansion port 114. The low-speed expansion port 114, which may includevarious communication ports (e.g., USB, Bluetooth, Ethernet, wirelessEthernet), may be coupled to one or more input/output devices, such as akeyboard, a pointing device, a scanner, or a networking device such as aswitch or router, e.g., through a network adapter.

The system 130 may be implemented in a number of different forms, asshown in FIG. 1. For example, it may be implemented as a standardserver, or multiple times in a group of such servers. Additionally, thesystem 130 may also be implemented as part of a rack server system or apersonal computer such as a laptop computer. Alternatively, componentsfrom system 130 may be combined with one or more other same or similarsystems and an entire system 140 may be made up of multiple computingdevices communicating with each other.

FIG. 1 also illustrates a user input system 140, in accordance with anembodiment of the invention. The user input system 140 includes aprocessor 152, memory 154, an input/output device such as a display 156,a communication interface 158, and a transceiver 160, among othercomponents. The user input system 140 may also be provided with astorage device, such as a microdrive or other device, to provideadditional storage. Each of the components 152, 154, 158, and 160, areinterconnected using various buses, and several of the components may bemounted on a common motherboard or in other manners as appropriate.

The processor 152 is configured to execute instructions within the userinput system 140, including instructions stored in the memory 154. Theprocessor may be implemented as a chipset of chips that include separateand multiple analog and digital processors. The processor may beconfigured to provide, for example, for coordination of the othercomponents of the user input system 140, such as control of userinterfaces, applications run by user input system 140, and wirelesscommunication by user input system 140.

The processor 152 may be configured to communicate with the user throughcontrol interface 164 and display interface 166 coupled to a display156. The display 156 may be, for example, a TFT LCD(Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic LightEmitting Diode) display, or other appropriate display technology. Thedisplay interface 156 may comprise appropriate circuitry and configuredfor driving the display 156 to present graphical and other informationto a user. The control interface 164 may receive commands from a userand convert them for submission to the processor 152. In addition, anexternal interface 168 may be provided in communication with processor152, so as to enable near area communication of user input system 140with other devices. External interface 168 may provide, for example, forwired communication in some implementations, or for wirelesscommunication in other implementations, and multiple interfaces may alsobe used.

The memory 154 stores information within the user input system 140. Thememory 154 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory may also be provided andconnected to user input system 140 through an expansion interface (notshown), which may include, for example, a SIMM (Single In Line MemoryModule) card interface. Such expansion memory may provide extra storagespace for user input system 140 or may also store applications or otherinformation therein. In some embodiments, expansion memory may includeinstructions to carry out or supplement the processes described aboveand may include secure information also. For example, expansion memorymay be provided as a security module for user input system 140 and maybe programmed with instructions that permit secure use of user inputsystem 140. In addition, secure applications may be provided via theSIMM cards, along with additional information, such as placingidentifying information on the SIMM card in a non-hackable manner. Insome embodiments, the user may use the applications to execute processesdescribed with respect to the process flows described herein.Specifically, the application executes the process flows describedherein.

The memory 154 may include, for example, flash memory and/or NVRAMmemory. In one aspect, a computer program product is tangibly embodiedin an information carrier. The computer program product containsinstructions that, when executed, perform one or more methods, such asthose described herein. The information carrier is a computer- ormachine-readable medium, such as the memory 154, expansion memory,memory on processor 152, or a propagated signal that may be received,for example, over transceiver 160 or external interface 168.

In some embodiments, the user may use the user input system 140 totransmit and/or receive information or commands to and from the system130 via the network 110. In this regard, the system 130 may beconfigured to establish a communication link with the user input system140, whereby the communication link establishes a data channel (wired orwireless) to facilitate the transfer of data between the user inputsystem 140 and the system 130. In doing so, the system 130 may beconfigured to access one or more aspects of the user input system 140,such as, a GPS device, an image capturing component (e.g., camera), amicrophone, a speaker, or the like.

The user input system 140 may communicate with the system 130 (and oneor more other devices) wirelessly through communication interface 158,which may include digital signal processing circuitry where necessary.Communication interface 158 may provide for communications under variousmodes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging,CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Suchcommunication may occur, for example, through radio-frequencytransceiver 160. In addition, short-range communication may occur, suchas using a Bluetooth, Wi-Fi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 170 mayprovide additional navigation- and location-related wireless data touser input system 140, which may be used as appropriate by applicationsrunning thereon, and in some embodiments, one or more applicationsoperating on the system 130.

The user input system 140 may also communicate audibly using audio codec162, which may receive spoken information from a user and convert it tousable digital information. Audio codec 162 may likewise generateaudible sound for a user, such as through a speaker, e.g., in a handsetof user input system 140. Such sound may include sound from voicetelephone calls, may include recorded sound (e.g., voice messages, musicfiles, etc.) and may also include sound generated by one or moreapplications operating on the user input system 140, and in someembodiments, one or more applications operating on the system 130.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in atechnical environment that includes a back end component (e.g., as adata server), that includes a middleware component (e.g., an applicationserver), that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components.

As shown in FIG. 1, the components of the system 140 and the user inputsystem 140 are interconnected using the network 110. The network 110,which may be include one or more separate networks, be a form of digitalcommunication network such as a telecommunication network, a local areanetwork (“LAN”), a wide area network (“WAN”), a global area network(“GAN”), the Internet, or any combination of the foregoing. It will alsobe understood that the network 110 may be secure and/or unsecure and mayalso include wireless and/or wired and/or optical interconnectiontechnology.

In accordance with an embodiments of the invention, the components ofthe system environment 100, such as the system 130 and the user inputsystem 140 may have a client-server relationship, where the user inputsystem 130 makes a service request to the system 130, the system 130accepts the service request, processes the service request, and returnsthe requested information to the user input system 140, and vice versa.This relationship of client and server typically arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other.

It will be understood that the embodiment of the system environment 100illustrated in FIG. 1 is exemplary and that other embodiments may vary.As another example, in some embodiments, the system environment mayinclude more, fewer, or different components. As another example, insome embodiments, some or all of the portions of the system environment100 may be combined into a single portion. Likewise, in someembodiments, some or all of the portions of the system 130 may beseparated into two or more distinct portions.

FIG. 2 illustrates a process flow for optimized data archival using datadetection and classification model 200, in accordance with an embodimentof the invention. As shown in block 202, the process flow includeselectronically receiving information associated with a first dataelement within a distributed network environment (e.g., systemenvironment 100). In some embodiments, the information associated withthe data element may include structural information describing the firstdata element. In one aspect, the structural information may include adata format in which the first data element is organized, processed,retrieved, and stored, linearity information describing the arrangementof the data records within the first data element (e.g., linear,non-linear, or the like), operational information describing whether thefirst data element is part of a static dataset or a dynamic dataset,volume information associated with first data element, data range,string patterns, data pattern describing the center, spread, shape, andother features of the first data element, and/or the like.

Next, as shown in block 204, the process flow includes determining afirst data type associated with the first data element based on at leastthe information associated with the first data element. In someembodiments, the system (e.g., system 130) may be configured toimplement a data detection and classification model to determine thedata type associated with the first data element. In this regard, thesystem may be configured to electronically receive informationassociated with one or more data elements within the distributed networkenvironment. Similar to the information associated with the first dataelement, the information associated with the one or more data elementsmay include structure information describing each data element. Thisinformation may be used to define a feature set where each piece ofinformation is used as a dimensional feature, resulting in amulti-dimensional feature set for each data element.

Having defined the multi-dimensional feature set for each data element,the system may be configured to electronically receive, from a computingdevice of a user (e.g., user input system 140), the one or more datatypes associated with the one or more data elements. In someembodiments, data types may be used as classification labels toimplement a supervised learning algorithm for training the machinelearning model. Next, the system may be configured to generate atraining dataset based on at least the information associated with theone or more data elements forming the multi-dimensional feature set andthe one or more data types forming the classification labels.

Once the training dataset is generated, the system may be configured toinitiate one or more machine learning algorithms on the trainingdataset. By initiating the machine learning algorithms on the trainingdataset, the system may be configured to generate a machine learningmodel. Using this machine learning model, the system may be configuredto classify any unseen data element into a specific data type.Accordingly, the system may be configured to electronically receiveinformation associated with the first data element. In response, thesystem may be configured to classify, using the machine learning model,the first data element into the first data type.

Next, as shown in block 206, the process flow includes determining oneor more archival actions associated with the first data element based onat least the first data type. In some embodiments, determining thearchival actions may include determining one or more target archivesassociated with the first data element based on at least the first datatype. In one aspect, determining the target archives may includedetermining the type of storage media (e.g., optical storage, cloudstorage, tape drives, portable hard drives, external solid statedevices, and/or the like) available for the first data element,determining the type of compression technique to be used duringarchival, determining the type of data archiving software application touse for the first data element, archival backup requirement for thefirst data element, and/or the like.

In some other embodiments, the system may be configured to determine thearchival actions based on the security level of the first data element.In this regard, the system may be configured to retrieve, from a headerportion of the first data element, a data security level associated withthe first data element. In one aspect, the data security level mayinclude sensitivity information of the first data element such asrestricted, high, medium, and low.

Next, as shown in block 208, the process flow includes determining oneor more archival requirements associated with the first data element. Insome embodiments, the one or more archival requirements associated withthe first data element may include one or more pre-configured conditionsfor archival of the first data element. In one aspect, the archivalrequirements may include a specific storage device requirement, aspecific data compression requirement, a conditioned archival backuprequirement, and/or the like. In some embodiments, the archivalrequirements may depend on the data security level associated with thefirst data element.

Next, as shown in block 210, the process flow includes determining oneor more utilization parameters associated with the first data element.In some embodiments, the system may be configured to determine a datausage pattern associated with the first data element. In this regard,the system may be configured to continuously scan the distributednetwork environment to retrieve instances of data usage of the firstdata element for a predetermined period of time. In one aspect, thesystem may be configured to identify specific hardware, software, andnetwork components within the network environment that are accessing thefirst data element during the predetermined period of time. In addition,the system may be configured to determine the rate at which the firstdata element is being accessed by the hardware, software, and networkcomponents. Based on the retrieved instances of data usage, the systemmay be configured to determine the utilization parameters for the firstdata element that may be used as archival triggers.

In some embodiments, the utilization parameters may include at least anidle time and a minimal disruption time period. In one aspect, the idletime may be defined as the amount of time the first data element remainsdormant in a specific storage location within network environment beforethe first data element is accessed by the hardware, software, andnetwork components. In some embodiments, the system may be configured todetermine the idle time for the first data element at every storagelocation within the network environment before the first data element isaccessed by the hardware, software, and network components. In anotheraspect, the system may be configured to determine a minimal disruptiontime period for the first data element to trigger the archival action toensure least disruption for the network environment.

Next, as shown in block 212, the process flow includes initiating anexecution of the one or more archiving actions on the first data elementbased on at least the one or more utilization parameters associated withthe first data element. By initiating the execution of the archivingactions based on the idle time and the minimal disruption time period,the system may be configured to ensure seamless retrieval of the firstdata element from the network environment for transfer to the targetarchives. By monitoring the data usage pattern to identify a specifictime frame for first data element retrieval, the system may beconfigured to reduce the amount of computing resources required tosuccessfully execute the archival actions.

Next, as shown in block 214, the process flow includes determining thatthe one or more archival actions meet the one or more archivalrequirements associated with the first data element. In someembodiments, the system may be configured to compare each archivalaction with a corresponding archival requirement to determine whetherthe archival action meets the archival requirement. In cases where thearchival action does not meet the archival requirement, the system maybe configured to transmit control signals configured to cause thecomputing device of the user to display an alert notification indicatingas such. In response, the system may be configured to electronicallyreceive, from the computing device of the user, one or more user inputsto reconfigure the one or more archival actions that previously did notmeet the archival requirements. In response to receiving the userinputs, the system may be configured to determine whether thereconfigured archival action meets the archival requirement. The step ofreconfiguring the archival actions based on the user input is donerepeatedly until the archival action meets the archival requirements.

Next, as shown in block 216, the process flow includes executing the oneor more archiving actions based on at least determining that the one ormore archival actions meet the one or more archival requirements. Insome embodiments, executing the one or more archiving actions mayinclude moving the first data element into the one or more targetarchives for long-term storage and retention. In one aspect, byexecuting the archiving actions based on the idle time and the minimaldisruption time period, the system may be configured to ensure seamlessretrieval of the first data element from the network environment andtransfer to the target archives.

In some embodiments, the system may be configured to generate a snapshotof the archived data elements. In this regard, the system may beconfigured to retrieve metadata associated with the execution of the oneor more archiving actions. In response, the system may be configured togenerate a dashboard report script to generate a graphical interface fordisplay on a computing device of a user. The graphical interface mayinclude information associated with the first data element and themetadata associated with the execution of the one or more archivingactions. When the user wishes to de-archive the first data element, theuser may view the snapshot of archival actions executed on the firstdata element before initiating de-archival actions.

FIG. 3 illustrates a data flow diagram for optimized data archival usingdata detection and classification model 300, in accordance with anembodiment of the invention. At step 302, the system implements a dataelement identification engine to determine the data type and the datasecurity level of the first data element. At step 304, the systemimplements the archival actions engine to determine the archival actionsto be executed on the first data element based on the data type and datasecurity level to transfer the first data element to the targetarchives. At step 306, the system determines the archival requirementsthat need to be met when executing the archival actions. At step 308,the system implements the data usage pattern detection engine to scanthe network environment and determine utilization parameters 308A suchas idle time and minimal disruption period. At step 310, the systemexecutes the archival actions. At step 312, in response to executing thearchival actions, the system initiates a metadata retrieval engine toretrieve metadata associated with the archival actions executed on thefirst data element. At step 314, the system initiates a dashboardpreview display engine to generate a snapshot of the archived dataelements, which includes information associated with the data elementsthat were archived and the corresponding metadata associated with thearchival actions executed when archiving the data elements.

As will be appreciated by one of ordinary skill in the art in view ofthis disclosure, the present invention may include and/or be embodied asan apparatus (including, for example, a system, machine, device,computer program product, and/or the like), as a method (including, forexample, a business method, computer-implemented process, and/or thelike), or as any combination of the foregoing. Accordingly, embodimentsof the present invention may take the form of an entirely businessmethod embodiment, an entirely software embodiment (including firmware,resident software, micro-code, stored procedures in a database, or thelike), an entirely hardware embodiment, or an embodiment combiningbusiness method, software, and hardware aspects that may generally bereferred to herein as a “system.” Furthermore, embodiments of thepresent invention may take the form of a computer program product thatincludes a computer-readable storage medium having one or morecomputer-executable program code portions stored therein. As usedherein, a processor, which may include one or more processors, may be“configured to” perform a certain function in a variety of ways,including, for example, by having one or more general-purpose circuitsperform the function by executing one or more computer-executableprogram code portions embodied in a computer-readable medium, and/or byhaving one or more application-specific circuits perform the function.

It will be understood that any suitable computer-readable medium may beutilized. The computer-readable medium may include, but is not limitedto, a non-transitory computer-readable medium, such as a tangibleelectronic, magnetic, optical, electromagnetic, infrared, and/orsemiconductor system, device, and/or other apparatus. For example, insome embodiments, the non-transitory computer-readable medium includes atangible medium such as a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a compact discread-only memory (CD-ROM), and/or some other tangible optical and/ormagnetic storage device. In other embodiments of the present invention,however, the computer-readable medium may be transitory, such as, forexample, a propagation signal including computer-executable program codeportions embodied therein.

One or more computer-executable program code portions for carrying outoperations of the present invention may include object-oriented,scripted, and/or unscripted programming languages, such as, for example,Java, Perl, Smalltalk, C++, SAS, SQL, Python, Objective C, JavaScript,and/or the like. In some embodiments, the one or morecomputer-executable program code portions for carrying out operations ofembodiments of the present invention are written in conventionalprocedural programming languages, such as the “C” programming languagesand/or similar programming languages. The computer program code mayalternatively or additionally be written in one or more multi-paradigmprogramming languages, such as, for example, F#.

Some embodiments of the present invention are described herein withreference to flowchart illustrations and/or block diagrams of apparatusand/or methods. It will be understood that each block included in theflowchart illustrations and/or block diagrams, and/or combinations ofblocks included in the flowchart illustrations and/or block diagrams,may be implemented by one or more computer-executable program codeportions. These one or more computer-executable program code portionsmay be provided to a processor of a general purpose computer, specialpurpose computer, and/or some other programmable data processingapparatus in order to produce a particular machine, such that the one ormore computer-executable program code portions, which execute via theprocessor of the computer and/or other programmable data processingapparatus, create mechanisms for implementing the steps and/or functionsrepresented by the flowchart(s) and/or block diagram block(s).

The one or more computer-executable program code portions may be storedin a transitory and/or non-transitory computer-readable medium (e.g. amemory) that can direct, instruct, and/or cause a computer and/or otherprogrammable data processing apparatus to function in a particularmanner, such that the computer-executable program code portions storedin the computer-readable medium produce an article of manufactureincluding instruction mechanisms which implement the steps and/orfunctions specified in the flowchart(s) and/or block diagram block(s).

The one or more computer-executable program code portions may also beloaded onto a computer and/or other programmable data processingapparatus to cause a series of operational steps to be performed on thecomputer and/or other programmable apparatus. In some embodiments, thisproduces a computer-implemented process such that the one or morecomputer-executable program code portions which execute on the computerand/or other programmable apparatus provide operational steps toimplement the steps specified in the flowchart(s) and/or the functionsspecified in the block diagram block(s). Alternatively,computer-implemented steps may be combined with, and/or replaced with,operator- and/or human-implemented steps in order to carry out anembodiment of the present invention.

Although many embodiments of the present invention have just beendescribed above, the present invention may be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Also, it will beunderstood that, where possible, any of the advantages, features,functions, devices, and/or operational aspects of any of the embodimentsof the present invention described and/or contemplated herein may beincluded in any of the other embodiments of the present inventiondescribed and/or contemplated herein, and/or vice versa. In addition,where possible, any terms expressed in the singular form herein aremeant to also include the plural form and/or vice versa, unlessexplicitly stated otherwise. Accordingly, the terms “a” and/or “an”shall mean “one or more,” even though the phrase “one or more” is alsoused herein. Like numbers refer to like elements throughout.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of and not restrictive on the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations, modifications, andcombinations of the just described embodiments can be configured withoutdeparting from the scope and spirit of the invention. Therefore, it isto be understood that, within the scope of the appended claims, theinvention may be practiced other than as specifically described herein.

What is claimed is:
 1. A system for optimized data archival using datadetection and classification model, the system comprising: at least onenon-transitory storage device; and at least one processing devicecoupled to the at least one non-transitory storage device, wherein theat least one processing device is configured to: electronically receiveinformation associated with a first data element within a distributednetwork environment; determine a first data type associated with thefirst data element based on at least the information associated with thefirst data element; determine one or more archival actions associatedwith the first data element based on at least the first data type;determine one or more archival requirements associated with the firstdata element; determine one or more utilization parameters associatedwith the first data element; initiate an execution of the one or morearchiving actions on the first data element based on at least the one ormore utilization parameters associated with the first data element;determine that the one or more archival actions meet the one or morearchival requirements associated with the first data element; andexecute the one or more archiving actions based on at least determiningthat the one or more archival actions meet the one or more archivalrequirements.
 2. The system of claim 1, wherein the at least oneprocessing device is further configured to: retrieve metadata associatedwith the execution of the one or more archiving actions; and initiate adashboard report script, wherein the dashboard report script isconfigured to generate a graphical interface for display on a computingdevice of a user, wherein the graphical interface comprises the metadataassociated with the execution of the one or more archiving actions. 3.The system of claim 2, wherein the at least one processing device isfurther configured to: electronically receive information associatedwith one or more data elements within the distributed networkenvironment; electronically receive, from the computing device of theuser, the one or more data types associated with the one or more dataelements; and generate a training dataset based on at least theinformation associated with the one or more data elements and the one ormore data types.
 4. The system of claim 3, wherein the at least oneprocessing device is further configured to: initiate one or more machinelearning algorithms on the training dataset; and generate a machinelearning model based on at least initiating the one or more machinelearning algorithms on the training dataset.
 5. The system of claim 4,wherein the at least one processing device is further configured to:electronically receive information associated with the first dataelement; and classify, using the machine learning model, the first dataelement into the first data type.
 6. The system of claim 1, wherein theat least one processing device is further configured to: determine oneor more archival actions associated with the first data element, whereindetermining further comprises: determining one or more target archivesassociated with the first data element based on at least the first datatype; and retrieving, from a header portion of the first data element,data security level associated with the first data element.
 7. Thesystem of claim 1, wherein the at least one processing device is furtherconfigured to: continuously scan the distributed network environment;determine a data usage pattern associated with the first data elementbased on at least continuously scanning the distributed networkenvironment; and determine one or more utilization parameters associatedwith the first data element based on at least the data usage pattern. 8.The system of claim 7, wherein the one or more utilization parameterscomprises at least an idle time and a minimal disruption time period. 9.A computer program product for optimized data archival using datadetection and classification model, the computer program productcomprising a non-transitory computer-readable medium comprising codecausing a first apparatus to: electronically receive informationassociated with a first data element within a distributed networkenvironment; determine a first data type associated with the first dataelement based on at least the information associated with the first dataelement; determine one or more archival actions associated with thefirst data element based on at least the first data type; determine oneor more archival requirements associated with the first data element;determine one or more utilization parameters associated with the firstdata element; initiate an execution of the one or more archiving actionson the first data element based on at least the one or more utilizationparameters associated with the first data element; determine that theone or more archival actions meet the one or more archival requirementsassociated with the first data element; and execute the one or morearchiving actions based on at least determining that the one or morearchival actions meet the one or more archival requirements.
 10. Thecomputer program product of claim 9, wherein the first apparatus isfurther configured to: retrieve metadata associated with the executionof the one or more archiving actions; and initiate a dashboard reportscript, wherein the dashboard report script is configured to generate agraphical interface for display on a computing device of a user, whereinthe graphical interface comprises the metadata associated with theexecution of the one or more archiving actions.
 11. The computer programproduct of claim 10, wherein the first apparatus is further configuredto: electronically receive information associated with one or more dataelements within the distributed network environment; electronicallyreceive, from the computing device of the user, the one or more datatypes associated with the one or more data elements; and generate atraining dataset based on at least the information associated with theone or more data elements and the one or more data types.
 12. Thecomputer program product of claim 11, wherein the first apparatus isfurther configured to: initiate one or more machine learning algorithmson the training dataset; and generate a machine learning model based onat least initiating the one or more machine learning algorithms on thetraining dataset.
 13. The computer program product of claim 12, whereinthe first apparatus is further configured to: electronically receiveinformation associated with the first data element; and classify, usingthe machine learning model, the first data element into the first datatype.
 14. The computer program product of claim 9, wherein the firstapparatus is further configured to: determine one or more archivalactions associated with the first data element, wherein determiningfurther comprises: determining one or more target archives associatedwith the first data element based on at least the first data type; andretrieving, from a header portion of the first data element, datasecurity level associated with the first data element.
 15. The computerprogram product of claim 9, wherein the first apparatus is furtherconfigured to: continuously scan the distributed network environment;determine a data usage pattern associated with the first data elementbased on at least continuously scanning the distributed networkenvironment; and determine one or more utilization parameters associatedwith the first data element based on at least the data usage pattern.16. The computer program product of claim 15, wherein the one or moreutilization parameters comprises at least an idle time and a minimaldisruption time period.
 17. A method for optimized data archival usingdata detection and classification model, the method comprising:electronically receiving information associated with a first dataelement within a distributed network environment; determining a firstdata type associated with the first data element based on at least theinformation associated with the first data element; determining one ormore archival actions associated with the first data element based on atleast the first data type; determining one or more archival requirementsassociated with the first data element; determining one or moreutilization parameters associated with the first data element;initiating an execution of the one or more archiving actions on thefirst data element based on at least the one or more utilizationparameters associated with the first data element; determining that theone or more archival actions meet the one or more archival requirementsassociated with the first data element; and executing the one or morearchiving actions based on at least determining that the one or morearchival actions meet the one or more archival requirements.
 18. Themethod of claim 17, wherein the method further comprises: retrievingmetadata associated with the execution of the one or more archivingactions; and initiating a dashboard report script, wherein the dashboardreport script is configured to generate a graphical interface fordisplay on a computing device of a user, wherein the graphical interfacecomprises the metadata associated with the execution of the one or morearchiving actions.
 19. The method of claim 18, wherein the methodfurther comprises: electronically receiving information associated withone or more data elements within the distributed network environment;electronically receiving, from the computing device of the user, the oneor more data types associated with the one or more data elements; andgenerate a training dataset based on at least the information associatedwith the one or more data elements and the one or more data types. 20.The method of claim 19, wherein the method further comprises: initiatingone or more machine learning algorithms on the training dataset; andgenerating a machine learning model based on at least initiating the oneor more machine learning algorithms on the training dataset.